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Structural stochastic volatility in asset pricing dynamics: Estimation and model contest

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  • Franke, Reiner
  • Westerhoff, Frank

Abstract

In the framework of small-scale agent-based financial market models, the paper starts out from the concept of structural stochastic volatility, which derives from different noise levels in the demand of fundamentalists and chartists and the time-varying market shares of the two groups. It advances several different specifications of the endogenous switching between the trading strategies and then estimates these models by the method of simulated moments (MSM), where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. In addition to the standard version of MSM with a quadratic loss function, we also take into account how often a great number of Monte Carlo simulation runs happen to yield moments that are all contained within their empirical confidence intervals. The model contest along these lines reveals a strong role for a (tamed) herding component. The quantitative performance of the winner model is so good that it may provide a standard for future research. --

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Bibliographic Info

Paper provided by Bamberg University, Bamberg Economic Research Group in its series BERG Working Paper Series with number 78.

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Date of creation: 2011
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Handle: RePEc:zbw:bamber:78

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Keywords: Method of simulated moments; moment coverage ratio; herding; discrete choice approach; transition probability approach;

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Citations

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Cited by:
  1. Westerhoff, Frank, 2011. "Interactions between the real economy and the stock market," BERG Working Paper Series 84, Bamberg University, Bamberg Economic Research Group.
  2. Wilko Bolt & Maria Demertzis & Cees Diks & Marco van der Leij, 2011. "Complex Methods in Economics: An Example of Behavioral Heterogeneity in House Prices," DNB Working Papers, Netherlands Central Bank, Research Department 329, Netherlands Central Bank, Research Department.
  3. Seregi, János & Lelovics, Zsuzsanna & Balogh, László, 2012. "The social welfare function of forests in the light of the theory of public goods," BERG Working Paper Series 87, Bamberg University, Bamberg Economic Research Group.
  4. Baur, Dirk G. & Glover, Kristoffer J., 2014. "Heterogeneous expectations in the gold market: Specification and estimation," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 40(C), pages 116-133.
  5. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Documents de travail du Centre d'Economie de la Sorbonne, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne 14031, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  6. Westerhoff, Frank & Franke, Reiner, 2012. "Agent-based models for economic policy design: Two illustrative examples," BERG Working Paper Series 88, Bamberg University, Bamberg Economic Research Group.
  7. Daniel Fricke & Thomas Lux, 2013. "The Effects of a Financial Transaction Tax in an Artificial Financial Market," Kiel Working Papers 1868, Kiel Institute for the World Economy.
  8. Neveu, Andre R., 2013. "Fiscal policy and business cycle characteristics in a heterogeneous agent macro model," Journal of Economic Behavior & Organization, Elsevier, Elsevier, vol. 92(C), pages 224-240.
  9. Corrado Di Guilmi & Xue-Zhong He & Kai Li, 2013. "Herding, Trend Chasing and Market Volatility," Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney 337, Quantitative Finance Research Centre, University of Technology, Sydney.
  10. Franke, Reiner & Westerhoff, Frank, 2011. "Why a simple herding model may generate the stylized facts of daily returns: Explanation and estimation," BERG Working Paper Series 83, Bamberg University, Bamberg Economic Research Group.
  11. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00983051, HAL.
  12. Liu, Yi-Fang & Zhang, Wei & Xu, Chao & Vitting Andersen, Jørgen & Xu, Hai-Chuan, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 407(C), pages 204-215.
  13. Schmitt, Noemi & Westerhoff, Frank, 2013. "Speculative behavior and the dynamics of interacting stock markets," BERG Working Paper Series 90, Bamberg University, Bamberg Economic Research Group.
  14. Carl Chiarella & Xue-Zhong He & Remco C.J. Zwinkels, 2014. "Heterogeneous Expectations in Asset Pricing:Empirical Evidence from the S&P500," Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney 344, Quantitative Finance Research Centre, University of Technology, Sydney.
  15. Yi-Fang Liu & Wei Zhang & Chao Xu & J{\o}rgen Vitting Andersen & Hai-Chuan Xu, 2013. "Impact of information cost and switching of trading strategies in an artificial stock market," Papers 1311.4274, arXiv.org, revised Jul 2014.
  16. Grosche, Stephanie & Heckelei, Thomas, 2014. "Price dynamics and financialization effects in corn futures markets with heterogeneous traders," Discussion Papers, University of Bonn, Institute for Food and Resource Economics 172077, University of Bonn, Institute for Food and Resource Economics.

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